import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




